In act of machine learning, a feature can be defined as a characteristic or a set of characteristics, that explains the occurrence of a phenomenon. When these characteristics are converted into some measurable form, they are called features. Feature engineering is creating new input features from your existing ones. Generally, you can think of data cleaning as a process of subtraction and feature engineering as a process of addition. Feature engineering can directly be defined as the process of creating new features from the existing features in a dataset. 

Building machine learning models can often be a complex and boring process. Involves many steps so B2Metric ML Studio is able to automate a certain percentage of feature engineering tasks, then the data scientists or the domain experts can focus on other aspects of the model. 

Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage. At the same time, B2Metric AI prepares data for modeling automatically, performing operations like one-hot encoding, missing data imputation, text mining, standardization, and data partitioning. If I had to do this by hand this would have taken several days, yet with automated machine learning, this only took me hardly any hours. Automated feature engineering key strength is when it’s applied to regrouping or reshaping data. This is why we recommend that you engage the creativity and experience of your business domain experts for domain-specific feature knowledge, such as how to correctly interpret the data.